funneljoin 
The goal of funneljoin is to make it easy to analyze behavior funnels.
For example, maybe you’re interested in finding the people who visit a
page and then register. Or you want all the times people click on an
item and add it to their cart within 2 days. These can all be answered
quickly with funneljoin’s after_join() or funnel_start() and
funnel_step(). As funneljoin uses dplyr, it can also work with remote
tables, but has only been tried on postgres.
For more examples of how to use funneljoin, check out the vignette, which shows different types of joins and the optional arguments, or this blog post, which showcases how to use funneljoin analyze questions and answers on StackOverflow.
Installation
You can install this package from GitHub with remotes:
library(remotes)
install_github("datacamp/funneljoin")after_join()
library(dplyr)
library(funneljoin)We’ll take a look at two tables that come with the package, landed and
registered. Each has a column user_id and timestamp.
Let’s say we wanted to get the first time people landed and the first
time afterward they registered. We would after_inner_join() with a
first-firstafter type:
landed %>%
after_inner_join(registered,
by_user = "user_id",
by_time = "timestamp",
type = "first-firstafter",
suffix = c("_landed", "_registered"))
#> # A tibble: 5 x 3
#> user_id timestamp_landed timestamp_registered
#> <dbl> <date> <date>
#> 1 1 2018-07-01 2018-07-02
#> 2 4 2018-07-01 2018-07-02
#> 3 3 2018-07-02 2018-07-02
#> 4 6 2018-07-07 2018-07-10
#> 5 5 2018-07-10 2018-07-11The first two arguments are the tables we’re joining, with the first table being the events that happen first. We then specify:
by_time: the time columns in each table. This would typically be a datetime or a date column. These columns are used to filter for time y being after or the same as time x.by_user:the user or identity columns in each table. These must be identical for a pair of rows to match.type: the type of funnel used to distinguish between event pairs, such as “first-first”, “last-first”, “any-firstafter”.suffix(optional): just like dplyr’s join functions, this specifies what should be appended to the names of columns that are in both tables.
type can be any combination of first, last, any, and
lastbefore with first, last, any, and firstafter. Some common
ones you may use include:
- first-first: Take the earliest x and y for each user before joining. For example, you want the first time someone entered an experiment, followed by the first time someone ever registered. If they registered, entered the experiment, and registered again, you do not want to include that person.
- first-firstafter: Take the first x, then the first y after that. For example, you want when someone first entered an experiment and the first course they started afterwards. You don’t care if they started courses before entering the experiment.
- lastbefore-firstafter: First x that’s followed by a y before the next x. For example, in last click paid ad attribution, you want the last ad someone clicked before the first subscription they did afterward.
- any-firstafter: Take all Xs followed by the first Y after it. For example, you want all the times someone visited a homepage and their first product page they visited afterwards.
- any-any: Take all Xs followed by all Ys. For example, you want all the times someone visited a homepage and all the product pages they saw afterward.
If your time and user columns have different names, you can work with that too:
landed <- landed %>%
rename(landed_at = timestamp,
user_id_x = user_id)
registered <- registered %>%
rename(registered_at = timestamp,
user_id_y = user_id)landed %>%
after_inner_join(registered,
by_user = c("user_id_x" = "user_id_y"),
by_time = c("landed_at" = "registered_at"),
type = "first-first")
#> # A tibble: 4 x 3
#> user_id_x landed_at registered_at
#> <dbl> <date> <date>
#> 1 1 2018-07-01 2018-07-02
#> 2 3 2018-07-02 2018-07-02
#> 3 6 2018-07-07 2018-07-10
#> 4 5 2018-07-10 2018-07-11funnel_start() and funnel_step()
Sometimes you have all the data you need in one table. For example, let’s look at this table of user activity on a website.
activity <- tibble::tribble(
~ "user_id", ~ "event", ~ "timestamp",
1, "landing", "2019-07-01",
1, "registration", "2019-07-02",
1, "purchase", "2019-07-07",
1, "purchase", "2019-07-10",
2, "landing", "2019-08-01",
2, "registration", "2019-08-15",
3, "landing", "2019-05-01",
3, "registration", "2019-06-01",
3, "purchase", "2019-06-04",
4, "landing", "2019-06-13"
)We can use funnel_start() and funnel_step() to make an activity
funnel. funnel_start() takes five arguments:
tbl: The table of events.moment_type: The first moment, or event, in the funnel.moment: The name of the column that indicates themoment_type.tstamp: The name of the column with the timestamps of the moment.user: The name of the column indicating the user who did the moment.
activity %>%
funnel_start(moment_type = "landing",
moment = "event",
tstamp = "timestamp",
user = "user_id")
#> # A tibble: 4 x 2
#> user_id timestamp_landing
#> <dbl> <chr>
#> 1 1 2019-07-01
#> 2 2 2019-08-01
#> 3 3 2019-05-01
#> 4 4 2019-06-13funnel_start() returns a table with the user_ids and a column with
the name of your timestamp column, _, and the moment type. This table
also includes metadata.
To add more moments to the funnel, you use funnel_step(). Since you’ve
indicated in funnel_start() what columns to use for each part, now you
only need to have the moment_type and the type of after_join()
(e.g. “first-first”, “first-any”).
activity %>%
funnel_start(moment_type = "landing",
moment = "event",
tstamp = "timestamp",
user = "user_id") %>%
funnel_step(moment_type = "registration",
type = "first-firstafter")
#> # A tibble: 4 x 3
#> user_id timestamp_landing timestamp_registration
#> <dbl> <chr> <chr>
#> 1 3 2019-05-01 2019-06-01
#> 2 4 2019-06-13 <NA>
#> 3 1 2019-07-01 2019-07-02
#> 4 2 2019-08-01 2019-08-15You can continue stacking on funnel_step() with more moments.
activity %>%
funnel_start(moment_type = "landing",
moment = "event",
tstamp = "timestamp",
user = "user_id") %>%
funnel_step(moment_type = "registration",
type = "first-firstafter") %>%
funnel_step(moment_type = "purchase",
type = "first-any")
#> # A tibble: 5 x 4
#> user_id timestamp_landing timestamp_registration timestamp_purchase
#> <dbl> <chr> <chr> <chr>
#> 1 3 2019-05-01 2019-06-01 2019-06-04
#> 2 1 2019-07-01 2019-07-02 2019-07-07
#> 3 1 2019-07-01 2019-07-02 2019-07-10
#> 4 2 2019-08-01 2019-08-15 <NA>
#> 5 4 2019-06-13 <NA> <NA>If you use a type that allows multiple moments of one type for a user,
like “first-any”, you will get more rows per user rather than more
columns. For example, user 1 had two purchases, so she now has two rows.
The timestamp_landing and timestamp_registration is the same for
both rows, but they have a different timestamp_purchase.
Finally, you can use the summarize_funnel() to understand how many and
what percentage of people make it through to each next step of the
funnel. We can also switch to funnel_steps() to shorten our code a
bit: we give it a character vector of moment_types in order and the
type for each step.
activity %>%
funnel_start(moment_type = "landing",
moment = "event",
tstamp = "timestamp",
user = "user_id") %>%
funnel_steps(moment_types = c("registration", "purchase"),
type = "first-firstafter") %>%
summarize_funnel()
#> # A tibble: 3 x 4
#> moment_type nb_step pct_cumulative pct_step
#> <fct> <int> <dbl> <dbl>
#> 1 landing 4 1 NA
#> 2 registration 3 0.75 0.75
#> 3 purchase 2 0.5 0.667nb_step is how many users made it to each step, pct_cumulative is
what percent that is out of the original step, and pct_step is what
percentage that is out of those who made it to the previous step. So in
our case, 2 people had a purchase, which is 50% of the people who landed
but 66% of those who registered.
Reporting bugs and adding features
If you find any bugs or have a feature request or question, please create an issue. If you’d like to add a feature, tests, or other functionality, please also make an issue first and let’s discuss!
funneljoin was developed at DataCamp by Anthony Baker, David Robinson, and Emily Robinson. It is now maintained by the DataCamp engineering team.